Estimated Time of Arrival Prediction via Modeling the Spatial-Temporal Interactions between Links and Crosses (GIS Cup)

Competition Page: DiDi-ETA

Feature Generation

python feature.py

Original train data should be stored at 'GISCUP-submit/data/raw_data/train/'. Original test data should be stored at 'GISCUP-submit/data/raw_data/'
In 'feature.py'. The variable ws on line 184 represents your workspace, which should be modified to your workspace path
This script will generate two feature files for each day's data, df and id_ time. They will be located in 'ws/data/feature/df/' and 'ws/data/feature/id_time/', respectively. You don't need to create these directories in advance. If these two directories don't exist, the script will generate them for you
An additional .pkl file will be created under /id_time directory

The df feature files contain information of each order and will be used in the W and D module of our model
There is sequence information in the id_time file. For each sequence, we elaborately insert each cross on a appropriate position into the link sequence
For the crosses inserted into the link sequence, we assigned new cross_id for them to make their ids and link_id has the same form. cross_dict.npy file records the mapping relationship between old and new IDS
This script will take a long time to run

python val_random_day.py

In 'val_random_day.py'. The variable ws on line 62 represents your workspace, which should be modified to your workspace path.
This script will generate two files in 'ws/data/feature/df/' and 'ws/data/feature/id_time/', respectively, which are the new validation set.

The script will traverse the generated feature files and randomly sample one thirtieth of the data from the feature file of each day as the validation data set.
The validation set produced will have a similar size and distribution to a single-day training set

This script should be run after the feature.py.

python train_val_test.py

In 'train_val_test.py'. The variable ws on line 409 represents your workspace, which should be modified to your workspace path
After running this file, you can get the result of a single model stored as "model_epoch2_day31.npy" in '~/GISCUP-submit/results

After running this file, you can get the result of a single model stored as "model_epoch2_day31.npy" in '~/GISCUP-submit/results
We adopt ensemble averaging to get the final prediction.